BackgroundAlpha-1-Antitrypsin Deficiency (AATD) is an economically unexplored genetic disease.MethodsDirect and indirect costs (based on self-reported information on healthcare utilization) and health-related quality of life (HRQL, as assessed by SGRQ, CAT, and EQ-5D-3 L) were compared between 131 AATD patients (106 with, 25 without augmentation therapy (AT)) and 2,049 COPD patients without AATD participating in the COSYCONET COPD cohort. The medication costs of AT were excluded from all analyses to reveal differences associated with morbidity profiles. The association of AATD (with/without AT) with costs or HRQL was examined using generalized linear regression modelling (GLM) adjusting for age, sex, GOLD grade, BMI, smoking status, education and comorbidities.ResultsAdjusted mean direct annual costs were €6,099 in AATD patients without AT, €7,117 in AATD patients with AT (excluding costs for AT), and €7,460 in COPD patients without AATD. AATD with AT was significantly associated with higher outpatient (+273%) but lower inpatient (−35%) and medication costs (−10%, disregarding AT) compared with COPD patients without AATD. There were no significant differences between groups regarding indirect costs and HRQL.ConclusionApart from AT costs, AATD patients tended to have lower, though not significant, overall costs and similar HRQL compared to COPD patients without AATD. AT was not associated with lower costs or higher HRQL.Trial registration NCT01245933 Electronic supplementary materialThe online version of this article (doi:10.1186/s12931-017-0543-8) contains supplementary material, which is available to authorized users.

Simulation modeling can be useful to estimate the long-term health and economic impacts of population-based dietary policies. We conducted a systematic scoping review following the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guideline to map and critically appraise economic evaluations of population-based dietary policies using simulation models. We searched Medline, Embase, and EconLit for studies published in English after 2005. Modeling studies were mapped based on model type, dietary policy, and nutritional target, and modeled risk factor–outcome pathways were analyzed. We included 56 studies comprising 136 model applications evaluating dietary policies in 21 countries. The policies most often assessed were reformulation (34/136), taxation (27/136), and labeling (20/136); the most common targets were salt/sodium (60/136), sugar-sweetened beverages (31/136), and fruit and vegetables (15/136). Model types included Markov-type (35/56), microsimulation (11/56), and comparative risk assessment (7/56) models. Overall, the key diet-related risk factors and health outcomes were modeled, but only 1 study included overall diet quality as a risk factor. Information about validation was only reported in 19 of 56 studies and few studies (14/56) analyzed the equity impacts of policies. Commonly included cost components were health sector (52/56) and public sector implementation costs (35/56), as opposed to private sector (18/56), lost productivity (11/56), and informal care costs (3/56). Most dietary policies (103/136) were evaluated as cost-saving independent of the applied costing perspective. An analysis of the main limitations reported by authors revealed that model validity, uncertainty of dietary effect estimates, and long-term intervention assumptions necessitate a careful interpretation of results. In conclusion, simulation modeling is widely applied in the economic evaluation of population-based dietary policies but rarely takes dietary complexity and the equity dimensions of policies into account. To increase relevance for policymakers and support diet-related disease prevention, economic effects beyond the health sector should be considered, and transparent conduct and reporting of model validation should be improved.

BackgroundPatient self-management is crucial to prevent complications and mortality in type 2 diabetes. From an economic perspective, time preference predicts short-sighted decision making and thus might help to explain non-adherence to self-anagement recommendations. However, recent studies on this association have shown mixed results.PurposeIn this study, we tested whether the combination of time preference and outcome expectancy can improve the predictions of self-management behavior.Patients and methodsData from 665 patients with type 2 diabetes were obtained from the cross-sectional KORA (Cooperative Health Research in the Region of Augsburg) GEFU 4 study. Time preference and outcome expectancy were measured by one question each, which were answered on a 4-point Likert scale. Their association with six self-managing behaviors was tested in logistic and linear regression analyses. Likewise, we examined the association between self-management and the interaction of outcome expectancy and time preference.ResultsA high time preference was associated with a significantly lower sum of self-management behaviors (β=−0.29, 95% CI [−0.54, −0.04]). Higher outcome expectancy was associated with a higher self-management score (β=0.21, 95% CI [−0.03, 0.45]). The interaction model showed that low time preference was only associated with better self-management when combined with a high outcome expectancy (β=0.05, 95% CI [−0.28, 0.39] vs β=0.27, 95% CI [−0.09, 0.63]).ConclusionTime preference and outcome expectancy are interrelated predictors of patient self-management and could be used to identify and to intervene on patients with a potentially poor self-management.

BackgroundPhysical inactivity (PIA) is an important risk factor for many chronic conditions and therefore might increase healthcare utilization and costs. This study aimed to analyze the association of PIA using device assessed and self-reported physical activity (PA) data with direct healthcare costs.MethodsCross-sectional data was retrieved from the population based KORA FF4 study (Cooperative Health Research in the Region of Augsburg) that was conducted in southern Germany from 2013 to 2014 (n = 2279). Self-reported PA was assessed with two questions regarding sports related PA in summer and winter and categorized into “high activity”, “moderate activity”, “low activity” and “no activity”. In a subsample (n = 477), PA was assessed with accelerometers and participants were categorized into activity quartiles (“very high”, “high”, “low” and “very low”) according to their mean minutes per day spent in light intensity, or in moderate-vigorous PA (MVPA). Self-reported healthcare utilization was used to estimate direct healthcare costs. We regressed direct healthcare costs on PA using a two-part gamma regression, adjusted for age, sex and socio-demographic variables. Additional models, including and excluding potential additional confounders and effect mediators were used to check the robustness of the results.ResultsAnnual direct healthcare costs of individuals who reported no sports PA did not differ from those who reported high sports PA [+€189, 95% CI: -188, 598]. In the subsample with accelerometer data, participants with very low MVPA had significantly higher annual costs than participants with very high MVPA [+€986, 95% CI: 15, 1982].ConclusionDevice assessed but not self-reported PIA was associated with higher direct healthcare costs. The magnitude and significance of the association depended on the choice of covariates in the regression models. Larger studies with device assessed PA and longitudinal design are needed to be able to better quantify the impact of PIA on direct healthcare costs.Electronic supplementary materialThe online version of this article (10.1186/s12889-018-5906-7) contains supplementary material, which is available to authorized users.

Highlights The study highlights avenues to target and improve self-management behavior (SMB)  An established SMB index comprising six dimensions and behavioral levels is used  Participation in self-management education programs is associated with better SMB  Older and obese respondents display worse SMB Abstract Aims:Self-management behavior (SMB) is an important aspect in the management of diabetes. This study aimed to identify sociodemographic and disease-related factors associated with good SMB in people with type 2 diabetes (T2D). Methods:We used data from 405 people with T2D aged 65 or older from the population-based KORA (Cooperative Health Research in the Area of Augsburg) Health Survey 2016 in Southern Germany.We estimated Poisson and logistic regression models testing the cross-sectional relationship between individual or disease-related characteristics and an established SMB sum index comprising six SMB dimensions stratified for people with and without insulin treatment. Results:Mean age in the sample was 75 and diabetes duration 13 years. The overall level of SMB was low.Higher SMB index scores were associated with higher age, treatment with insulin, participation in a diabetes education program, and, for people with insulin treatment, with a BMI<30kg/m 2 . Single item analyses generally supported these findings. Conclusions:SMB in people with T2D needs to be improved with efficient interventions. Targeting obese individuals and those at an early stage of the disease with low-barrier, regular education or selfmanagement programs may be a preferred strategy.